The libalf library is a comprehensive, open-source library for learning finite-state automata covering various well-known learning techniques (such as Angluin's L*, Biermann's learning approach, and RPNI), as well as novel learning algorithms (e.g. for NFA and visibly one-counter automata).

Please refer to the About-page for more information about libalf.

libalf in action

We are happy that various researchers are already using libalf for their projects. You may want to have a look at the following papers, which give an impression about what libalf can be used for.

• L. Feng, M. Kwiatkowska, and D. Parker. Compositional verification of probabilistic systems using learning. In the Seventh International Conference on the Quantitative Evaluation of Systems, pages 133 to 142, IEEE, 2010.
• J. Esparza, M. Leucker, and M. Schlund. Learning Workflow Petri Nets. In Applications and Theory of Petri Nets, pages 206 to 225, Springer, 2010.
• M. Merten, B. Steffen, F. Howar, and T. Margaria. Next Generation LearnLib. In Tools and Algorithms for the Construction and Analysis of Systems, pages 220 to 223, Springer, 2011.
• D. N. Reachability Games on Automatic Graphs. In 15th International Conference on Implementation and Application of Automata, pages 222 to 230, Springer, 2011.

In-development notice

Please note that libalf (and the libalf website) are still in development. In particular, we are currently hard working on a comprehensive documentation.

Please come back later or subscribe to our feed to stay informed.